import inspect
import os
import unittest

import torch

import accelerate
from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess_async, require_multi_gpu
from accelerate.utils import patch_environment




class   _UpperCamelCase (		unittest.TestCase					):


			"""simple docstring"""






			def 			_SCREAMING_SNAKE_CASE     (  self		)	->   Optional[int]:

							'''simple docstring'''


							__lowercase					     =				inspect.getfile(accelerate.test_utils		)
							__lowercase					     =				os.path.sep.join(mod_file.split(os.path.sep		)[:-1] + ['''scripts''', '''test_script.py''']		)
							__lowercase					     =				os.path.sep.join(
							    mod_file.split(os.path.sep		)[:-1] + ['''scripts''', '''test_distributed_data_loop.py''']		)
							__lowercase					     =				os.path.sep.join(mod_file.split(os.path.sep		)[:-1] + ['''scripts''', '''test_ops.py''']		)





			@require_multi_gpu
			def 			_SCREAMING_SNAKE_CASE     (  self		)	->   Optional[Any]:

							'''simple docstring'''


							print(F"Found {torch.cuda.device_count()} devices."		)
							__lowercase					     =				['''torchrun''', F"--nproc_per_node={torch.cuda.device_count()}", self.test_file_path]
							with patch_environment(omp_num_threads=1		):
											execute_subprocess_async(lowerCAmelCase__ ,  env=os.environ.copy()		)





			@require_multi_gpu
			def 			_SCREAMING_SNAKE_CASE     (  self		)	->   Tuple:

							'''simple docstring'''


							print(F"Found {torch.cuda.device_count()} devices."		)
							__lowercase					     =				['''torchrun''', F"--nproc_per_node={torch.cuda.device_count()}", self.operation_file_path]
							print(F"Command: {cmd}"		)
							with patch_environment(omp_num_threads=1		):
											execute_subprocess_async(lowerCAmelCase__ ,  env=os.environ.copy()		)





			@require_multi_gpu
			def 			_SCREAMING_SNAKE_CASE     (  self		)	->   List[str]:

							'''simple docstring'''


							__lowercase					     =				['''torchrun''', F"--nproc_per_node={torch.cuda.device_count()}", inspect.getfile(self.__class__		)]
							with patch_environment(omp_num_threads=1		):
											execute_subprocess_async(lowerCAmelCase__ ,  env=os.environ.copy()		)





			@require_multi_gpu
			def 			_SCREAMING_SNAKE_CASE     (  self		)	->   List[Any]:

							'''simple docstring'''


							print(F"Found {torch.cuda.device_count()} devices, using 2 devices only"		)
							__lowercase					     =				['''torchrun''', F"--nproc_per_node={torch.cuda.device_count()}", self.data_loop_file_path]
							with patch_environment(omp_num_threads=1 ,  cuda_visible_devices='''0,1'''		):
											execute_subprocess_async(lowerCAmelCase__ ,  env=os.environ.copy()		)


if __name__ == "__main__":
		__a    :	Tuple         =    Accelerator()
		__a    :	List[str]         =    (accelerator.state.process_index + 2, 1_0)
		__a    :	Union[str, Any]         =    torch.randint(0, 1_0, shape).to(accelerator.device)

		__a    :	Dict         =    """"""

		__a    :	Optional[Any]         =    accelerator.pad_across_processes(tensor)
		if tensora.shape[0] != accelerator.state.num_processes + 1:
				error_msg += F"Found shape {tensora.shape} but should have {accelerator.state.num_processes + 1} at dim 0."
		if not torch.equal(tensora[: accelerator.state.process_index + 2], tensor):
				error_msg += "Tensors have different values."
		if not torch.all(tensora[accelerator.state.process_index + 2 :] == 0):
				error_msg += "Padding was not done with the right value (0)."

		__a    :	Optional[Any]         =    accelerator.pad_across_processes(tensor, pad_first=True)
		if tensora.shape[0] != accelerator.state.num_processes + 1:
				error_msg += F"Found shape {tensora.shape} but should have {accelerator.state.num_processes + 1} at dim 0."
		__a    :	Dict         =    accelerator.state.num_processes - accelerator.state.process_index - 1
		if not torch.equal(tensora[index:], tensor):
				error_msg += "Tensors have different values."
		if not torch.all(tensora[:index] == 0):
				error_msg += "Padding was not done with the right value (0)."

		# Raise error at the end to make sure we don't stop at the first failure.
		if len(error_msg) > 0:
				raise ValueError(error_msg)
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__a    :	Tuple         =    {
    """configuration_luke""": ["""LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LukeConfig"""],
    """tokenization_luke""": ["""LukeTokenizer"""],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__a    :	Dict         =    [
		    """LUKE_PRETRAINED_MODEL_ARCHIVE_LIST""",
		    """LukeForEntityClassification""",
		    """LukeForEntityPairClassification""",
		    """LukeForEntitySpanClassification""",
		    """LukeForMultipleChoice""",
		    """LukeForQuestionAnswering""",
		    """LukeForSequenceClassification""",
		    """LukeForTokenClassification""",
		    """LukeForMaskedLM""",
		    """LukeModel""",
		    """LukePreTrainedModel""",
		]


if TYPE_CHECKING:
		from .configuration_luke import LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP, LukeConfig
		from .tokenization_luke import LukeTokenizer

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_luke import (
				    LUKE_PRETRAINED_MODEL_ARCHIVE_LIST,
				    LukeForEntityClassification,
				    LukeForEntityPairClassification,
				    LukeForEntitySpanClassification,
				    LukeForMaskedLM,
				    LukeForMultipleChoice,
				    LukeForQuestionAnswering,
				    LukeForSequenceClassification,
				    LukeForTokenClassification,
				    LukeModel,
				    LukePreTrainedModel,
				)

else:
		import sys

		__a    :	Any         =    _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)